To explain the output of a DL model for an individual input example, the influence of input values on the output can be quantified.
Attribution techniques aim to compute the relevance of each input value for the output of the DNN [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}.
The relevance values are then visualized as a superimposed heat map on the input.
Commonly, the relevance heat maps are referred to as saliency maps [2]}.
Due to the complexity of DNN, it is infeasible to compute the relevance values exactly.
Therefore, various attribution techniques have been suggested that, for example, compute gradients of the output with respect to the input values [1]}, combine gradient information with activation values [5]} or decompose the output [11]}.
Saliency maps are only interpretable if the input data are visually interpretable themselves because the saliency values are superimposed on the input.
Hence, saliency maps are particularly suitable for image data and data that can be interpreted as image, for example, audio data as spectrograms [12]}, [13]}, [14]}.
Attribution techniques are convenient due to the ease of visually interpreting them but have disadvantages, as well.
Because saliency maps are computed for individual examples only, it is difficult to draw conclusions about how the model performs its task in general.
Moreover, some attribution techniques can be misleading because their relevance computation is not strongly enough related to the output [15]}, [16]}, [17]}.
